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  • 1.
    Garrison, Thomas G.
    et al.
    Umeå University, Faculty of Arts, Humlab.
    Chapman, Bruce
    Houston, Stephen
    Roman, Edwin
    Garrido Lopez, Jose Luis
    Discovering ancient Maya settlements using airborne radar elevation data2011In: Journal of Archaeological Science, ISSN 0305-4403, E-ISSN 1095-9238, Vol. 38, no 7, 1655-1662 p.Article in journal (Refereed)
    Abstract [en]

    This report presents the results of using NASA/JPL airborne synthetic aperture radar data (AIRSAR) to detect ancient Maya settlements beneath jungle canopy in Guatemala. AIRSAR stands out from previous applications of radar remote sensing in the Maya lowlands because of its canopy-penetrating capabilities. The authors offer an overview of the AIRSAR technology, followed by a case study in which the AIRSAR data receive testing in the field. Reconnaissance in the region around the Maya site of El Zotz led to the discovery of two new sites, including the medium-sized settlement of La Avispa. AIRSAR also aided archaeologists in detecting zones of residential settlement around the site core of El Zotz. This research will serve as a guide for future applications of radar remote sensing in Maya archaeology. (C) 2011 Elsevier Ltd. All rights reserved.

  • 2.
    Haake, Johan
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Björk, Peter
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Karlsson, Johannes
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Demonstration abstract: positioning by synchronized IR light2014In: Proceedings of the 13th International Symposium on Information Processing in Sensor Networks (IPSN' 14), IEEE conference proceedings, 2014, 335-336 p.Conference paper (Refereed)
    Abstract [en]

    In this paper we will describe a positioning system based on synchronized IR light. Each node will be assigned a timeslot where they will send out an IR light. There is an IR camera that is also synchronized to the timeslots that will detect the position of each node and the ID that corresponds to the timeslot. To synchronize the clock of all nodes an IR flashlight is sent out that is detected by a photodiode on the nodes. The demo will show live video stream from a network camera where the ID and position of each node in view will be overlaid in real-time in the video.

  • 3.
    Mattias, Tjernqvist
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Backpack-based inertial navigation and LiDAR mapping in forest environments2017Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Creating 3D models of our surrounding world has seen a rapid increase in research and development over the last few years. A common method is to use laser scanners. Mapping is done either by ground based systems or airborne systems. With stationary ground-based laser scanning, or terrestrial laser scanning (TLS), it is possible to obtain high accuracy point clouds. But stationary TLS can often be a cumbersome and time-demanding task due to its lack of mobility. Because of this, much research has gone into mobilised TLS systems, referred commonly to as mobile laser scanning (MLS). Georeferencing point clouds to a world coordinate system is a difficult task in environments where global navigation satellite systems (GNSS) is unreliable. One such environment is forests, where the GNSS signal can be blocked, absorbed or reflected from the trees and canopy. Accurate georeference of points clouds for MLS systems in forests is difficult task that can be solved by using additional measurement instruments and post-processing algorithms to reduce the accumulation of errors, also known as drift. In this thesis a backpack-based MLS system to be used in forests was tested. The MLS system was composed of a GNSS, an inertial navigation unit (INS) and a laser scanner. The collected data was post-processed and analyzed to reduce the effects of detecting multiple ground layers and multiples of the same tree due to drift. The post-processing algorithm calculated tree and ground features to be used for adjusting the point cloud in the horizontal and vertical planes. The forest survey was done for an area roughly 40 meters in diameter. The MLS data was compared against TLS data as well as manual caliper data - where the caliper data was only measured in an area roughly 24 meters in diameter. The results indicated that the effects of multiple ground layers and multiple tree copies were removed after post-processing. Out of the total 214 TLS trees, 185 managed to be co-registered to MLS trees. The root mean square error (RMSE) and bias of the diameter at breast height (DBH) between the MLS andTLS data were 27.00 mm and -9.33 mm respectively. Co-registrationof the MLS and manual caliper data set gave 36 successful matches out of the total 43 manually measured DBH. The DBH RMSE and bias were 16.95 mm and -10.58 mm respectively. A Swedish TLS forest study obtained a DBH RMSE and bias (between TLS and caliper) of approximately 10 mm and +0.06 mm respectively. A Finnish backpack MLS forest study obtained a DBH RMSE and bias (between MLS and TLS) of 50.6 mm and +11.1 mm respectively. Evaluating the difference in radius at different heights along the tree stems between the MLS and TLS revealed a slight dependence on height, as the radius difference increased slightly closer to the stem base. The results indicated that backpack-based MLS systems has the potential for accurate lidar mapping in forests, and future development is of great interest to improve this system further.

  • 4. Olsson, Håkan
    et al.
    Skånes, Helle
    Tulldahl, Michael
    Wikström, Sofia
    Kautsky, Hans
    Rydell, Bengt
    Årnfelt, Erik
    Eklöf, Jan
    Steinvall, Ove
    Flygburen laser och digitala bilder för kartering och övervakning av akvatisk och terrester miljö2014Report (Other academic)
  • 5.
    Yu, Jun
    et al.
    Biostokastikum, SLU.
    Ranneby, Bo
    Biostokastikum, SLU.
    Nonparametric and probabilistic classification of agricultural crops using multitemporal images2006Conference paper (Refereed)
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